Article
Engineering, Aerospace
Ankit Goel, Dennis S. Bernstein
Summary: In applications of state estimation, it is often necessary to restrict the state correction to a specific subspace corresponding to the measurement location. This paper presents the injection-constrained unscented Kalman filter (IC-UKF) and the injection-constrained retrospective cost filter (IC-RCF) to address this problem. The performance of these filters is evaluated numerically, and their accuracy and suboptimality relative to full-state output-error injection are compared.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2022)
Article
Biochemical Research Methods
Amir H. Abolmasoumi, Mohammad Mohammadian, Lamine Mili
Summary: This paper proposes a revised version of the GM-UKF for state estimation in GRNs with different deviations from assumptions. The GM-UKF outperforms other methods for all outlier types, while the H-8-UKF is appropriate for changes in noise powers.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Automation & Control Systems
Zixing Liu, Ziyun Wang, Yan Wang, Zhicheng Ji
Summary: This study presents constrained zonotopic Kalman filter-based fault estimators for additive and multiplicative sensor faults in constrained systems. Fault detection is achieved by checking if the output value falls within the estimated interval. Experimental results show the effectiveness of the algorithm and its advantages.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Junling Wang, Xiaobing Ma, Yongbo Zhang, Da Huang
Summary: A novel constrained two-stage Kalman filter algorithm is proposed in this paper to address the problem of large errors in system state estimation caused by non-deterministic measurement noise properties. The algorithm establishes a covariance update model based on the prior estimate of system states and introduces a constraint algorithm to accurately estimate the measurement noise covariance. The results show that the proposed method is more effective than conventional methods when facing the issue of time-varying measurement noise covariance.
Article
Robotics
Seyed Fakoorian, Angel Santamaria-Navarro, Brett T. Lopez, Dan Simon, Ali-akbar Agha-mohammadi
Summary: This work presents a resilient and adaptive state estimation framework, AMCCKF, for robots operating in perceptually-degraded environments, which is able to robustly handle corrupted measurements and adjust filter parameters online for improved performance. Two methods are developed, modifying noise models and kernel bandwidth based on measurement quality, with differences in computational complexity and overall performance. The framework is validated through real experiments on aerial and ground robots, forming part of the solution used in the DARPA Subterranean Challenge by the COSTAR team.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Energy & Fuels
Prashant Shrivastava, Tey Kok Soon, Mohd Yamani Idna Bin Idris, Saad Mekhilef, Syed Bahari Ramadzan Syed Adnan
Summary: In this work, a highly accurate and computationally efficient model-based battery states estimation method is proposed. It can concurrently estimate different battery states and has been validated with experimental results for accuracy and computational efficiency.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Xiaobo Zhao, Kangsan Kim, Seunghun Jung
Summary: This paper proposed a new method to improve the accuracy of SOC estimation for VRFBs through data fusion, and experimental results demonstrated the accuracy and reliability of this method.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Multidisciplinary Sciences
Kevin Course, Prasanth B. Nair
Summary: This study presents a state estimation technique based on approximate Bayesian approach, which learns the missing terms and state estimation in the mathematical model. It enables state estimation for physical systems with partially or completely unknown dynamical equations.
Article
Engineering, Aerospace
Marzieh Ghani, Nima Assadian, Renuganth Varatharajoo
Summary: This paper presents a novel approach to simultaneously estimate the attitude and deformation of a flexible satellite using only sun sensor and magnetometer measurements. The results show that the algorithm successfully estimates the deformation without the need for conventional sensors.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Engineering, Mechanical
Tianhao Yu, Zhiheng Wang, Jingfeng Wang
Summary: In this study, a novel iterative augmented unscented Kalman filter (IAUKF) is developed for simultaneous state-parameter-input estimation, overcoming the limitations of existing methods with improved computational efficiency.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Muhammad Rashed, Iqbal Gondal, Joarder Kamruzzaman, Syed Islam
Summary: State Estimation is a traditional and reliable technique used in power distribution and control systems. This paper proposes a novel state estimator based on Kalman filter to protect against False Data Injection Attacks, showing superiority over traditional methods in simulation experiments.
Article
Computer Science, Artificial Intelligence
Harikumar Kandath, Md Meftahul Ferdaus, Zhen Wei Ng, Bangjian Zhou, Suresh Sundaram, Xiaoli Li, Senthilnath Jayavelu
Summary: Researchers have developed intelligent Kalman filters (KFs) by combining them with machine learning algorithms for accurate estimation of states in dynamical systems. In this paper, a parsimonious autonomous sequential estimator (PASE) is proposed, which combines a KF-based estimator and an autonomous-structured recurrent parsimonious learning machine (rPALM) in a sequential manner. The performance evaluation shows that PASE provides better estimation accuracy with a compact architecture for both linear and nonlinear dynamical systems, making it suitable for real-world applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Li Li, Mingyang Fan, Yuanqing Xia, Cui Zhu
Summary: This paper focuses on distributed fusion estimation for a multi-sensor nonlinear stochastic system, proposing an event-trigger mechanism and unscented Kalman filters for fusion estimation. It establishes boundedness conditions for fusion estimation error covariance through a recursive algorithm and trigger threshold. An ideal compromise between communication rate and estimation performance is achieved.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Multidisciplinary
Liu YiWen, Shi Qin, Wei YuJiang, He ZeJia, Hu XiaoSong, He Lin
Summary: This paper proposes a square root cubature particle filter approach to estimate the state of charge of lithium-ion battery. By using a fractional order model and particle swarm optimization algorithm, the accuracy and computational efficiency of the estimation are improved. Experimental results demonstrate that the proposed approach has better accuracy and robustness.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Energy & Fuels
Baran Celikten, Ozan Eren, Yusuf Selim Karatas
Summary: This article reviews and compares different algorithms for state of charge (SOC) estimation in the battery management system (BMS). It focuses on Coulomb Counting (CC) and Sigma Point Kalman Filter (SPKF) methods and proposes a hybrid approach to leverage their advantages. The results show that using SPKF alone is computationally expensive, while using CC alone may lead to inaccurate predictions. The article also analyzes execution time and suggests the possibility of downsizing the processor.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Automation & Control Systems
Xiaoyu Luo, Chengcheng Zhao, Chongrong Fang, Jianping He
Summary: This paper investigates the problem of false data injection attacks in multi-agent dynamical systems and proposes FDI attack set selection algorithms to maximize the convergence error by finding the optimal subset of compromised agents.
Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Automation & Control Systems
Alexander Aleksandrov
Summary: This paper investigates the absolute stability problem for positive Persidskii systems with delay, proposes a special construction method for diagonal Lyapunov-Krasovskii functionals, and derives a criterion for the existence of such functionals guaranteeing the absolute stability, as well as obtaining sufficient conditions for a family of time-delay Persidskii systems to construct a common diagonal Lyapunov-Krasovskii functional. The efficiency of the developed approaches is demonstrated through four examples.
Article
Automation & Control Systems
Noureddine Toumi, Roland Malhame, Jerome Le Ny
Summary: This paper addresses large multi-agent dynamic discrete choice problems using a linear quadratic mean field games framework. The model incorporates the features where agents have to reach a predefined set of possible destinations within a fixed time frame and running costs can become negative to simulate crowd avoidance. An upper bound on the time horizon is derived to prevent agents from escaping to infinity in finite time. The existence of a Nash equilibrium for infinite population and its epsilon-Nash property for a large but finite population are established. Simulations are conducted to explore the model behavior in various scenarios.
Article
Automation & Control Systems
Philippe Schuchert, Vaibhav Gupta, Alireza Karimi
Summary: This paper presents the design of fixed-structure controllers for the As2 and Asw synthesis problem using frequency response data. The minimization of the norm of the transfer function between the exogenous inputs and performance outputs is approximated through a convex optimization problem involving Linear Matrix Inequalities (LMIs). A general controller parametrization is used for continuous and discrete-time controllers with matrix transfer function or state-space representation. Numerical results show that the proposed data-driven method achieves performance equivalent to model-based approaches when a parametric model is available.
Correction
Automation & Control Systems
Zhijun Guo, Gang Chen
Article
Automation & Control Systems
Matteo Della Rossa, Thiago Alves Lima, Marc Jungers, Raphael M. Jungers
Summary: This paper presents new stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. The study focuses on two specific settings: the robust case with completely unknown and unobservable active mode, and the mode-dependent case with controller depending on the current active switching mode. The technical developments are based on graph-theory tools and path-complete Lyapunov functions framework, enabling the design of robust and mode-dependent piecewise linear state-feedback controllers using directed and labeled graphs.
Article
Automation & Control Systems
Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, W. P. M. H. (Maurice) Heemels
Summary: This study investigates a scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. By designing both the observer and the local transmission policies, accurate state estimates can be obtained while only sporadically using the communication network.
Article
Automation & Control Systems
Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Summary: This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. The approach constructs a finite-state abstraction of the original system and synthesises a controller for the abstraction. It introduces the robust stutter bisimulation relation to account for disturbances and uncertainty, ensuring that related states have similar effects under the same controller. The paper demonstrates that the existence of a controller for the abstracted system implies the existence of a controller for the original system enforcing the linear temporal logic formula.
Article
Automation & Control Systems
Clement Chahbazian, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Aurelien Blanc, Christian Musso
Summary: The paper derives a recursive formula of the Fisher information matrix on Lie groups and applies it to nonlinear Gaussian systems on Lie groups for testing. The proposed recursive CRLB is consistent with state-of-the-art filters and exhibits representative behavior in estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups, which is fundamental for implementing robust algorithms.
Article
Automation & Control Systems
Yiheng Fu, Pouria Ramazi
Summary: This study investigates the characteristics of decision fluctuations in heterogeneous populations and explores the uncertainties in imitation behavior. The findings are important for understanding the bounded rationality nature of imitation behaviors.
Article
Automation & Control Systems
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Summary: This paper introduces a mathematical relationship between the accuracy of reduced-order linear-time invariant subsystem models and the stability and accuracy of the resulting reduced-order interconnected linear time-invariant model. This result can be used to directly translate the accuracy characteristics of the reduced-order subsystem models to the accuracy properties of the interconnected reduced-order model, or to translate accuracy requirements on the interconnected system model to accuracy requirements on subsystem models.
Article
Automation & Control Systems
Piyush Gupta, Vaibhav Srivastava
Summary: We study the optimal fidelity selection for a human operator servicing tasks in a queue, considering the trade-off between high-quality service and penalty due to increased queue length. By modeling the operator's cognitive dynamics and task fidelity, we determine the optimal policy and value function numerically, and analyze the structural properties of the optimal fidelity policy.
Article
Automation & Control Systems
Lukas Schwenkel, Alexander Hadorn, Matthias A. Mueller, Frank Allgoewer
Summary: In this work, the authors study economic model predictive control (MPC) in periodic operating conditions. They propose a method to achieve optimality by multiplying the stage cost by a linear discount factor, which is easy to implement and robust against online changes. Under certain assumptions, they prove that the resulting linearly discounted economic MPC achieves optimal asymptotic average performance and guarantees practical asymptotic stability of the optimal periodic orbit.
Article
Automation & Control Systems
Taher Ebrahim, Sankaranarayanan Subramanian, Sebastian Engell
Summary: We propose a robust nonlinear model predictive control algorithm for dynamic systems with mixed degrees of freedom. This algorithm optimizes both continuous and discrete manipulated variables, enhancing closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy and provides sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. The effectiveness of the approach is demonstrated through two nonlinear simulation examples.